System and method for frequency object enablement in self-organizing networks

ABSTRACT

Systems and methods provide automated generation of neighbor frequency lists for configuration of frequency objects for wireless stations. A computing device selects a target sector carrier of a wireless station of multiple wireless stations in a radio access network and identifies, based on distances from the wireless station, neighboring sector carriers of the target sector carrier. The computing device filters the neighboring sector carriers based on an azimuth of the target sector carrier to form a filtered set of neighboring sector carriers. The computing device calculates a probability of neighboring frequencies for the target sector carrier based on locations of the filtered set of neighboring sector carriers and generates, based on the calculating, a neighbor frequency list for the target sector carrier. The neighbor frequency list is used to configure frequency objects, for the target sector carrier, that ensure seamless handovers within the radio access network.

BACKGROUND INFORMATION

Providers of wireless communication services continue to improve andexpand available wireless networks. One aspect of such improvementsincludes the development of wireless access networks as well as optionsto utilize such wireless access networks. A wireless access network maymanage a large number of devices. For example, a base station (alsoreferred to as a wireless station) may service a large number of mobiledevices, connecting these devices to a core network. The mobile devicesmay move within the wireless access network, using different basestations to maintain a connection with the core network. During anexecution of a handover procedure between wireless stations, varioussignaling may take place between a source wireless station and a newwireless station, as well as signaling with a mobile device. The successor failure of the handover procedure can impact the quality of serviceprovided to the mobile device.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating an environment according to animplementation described herein;

FIG. 2 is a diagram illustrating different sectors and channels for awireless station of FIG. 1;

FIG. 3A is a diagram illustrating a wireless handover;

FIG. 3B is a diagram illustrating a geographic environment withdifferent morphologies for wireless stations;

FIG. 3C is an illustration of a frequency border according to animplementation;

FIG. 4 is a diagram illustrating exemplary components of a device thatmay be included in a component of FIG. 1 according to an implementationdescribed herein;

FIG. 5 is a diagram illustrating exemplary components of the modelingsystem of FIG. 1;

FIG. 6A is a table indicating different morphology categories anddistance assignments that can be used by the geographical neighborselector of FIG. 5;

FIG. 6B is a simplified example of a filter that be applied by thedirectional filter of FIG. 5;

FIG. 7 is a diagram illustrating exemplary components of the sectordatabase of FIG. 5 according to an implementation described herein; and

FIG. 8 is a flow diagram illustrating an exemplary process forgenerating a neighboring frequency list for a target carrier sector,according to an implementation described herein.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

The following detailed description refers to the accompanying drawings.The same reference numbers in different drawings identify the same orsimilar elements.

A handoff (or handover) refers to the process of transferring an activecall or data session from one cell in a cellular network to another orfrom one channel in a cell to another. A well-implemented handoff isimportant for delivering uninterrupted service to a voice or datasession user.

Each wireless station typically uses multiple carrier frequencies in asingle instance. For example, a single wireless station may providecoverage over an area referred to as a cell. A cell typically usesmultiple carrier frequencies to meet capacity demands and provideguaranteed service quality within each cell. It is not cost effective todeploy all carrier frequencies on every cell a wireless carrier owns ina particular area. A cell may be divided into one or more sectors, witheach sector providing different areas of coverage that may overlap. Aparticular sector may also transmit and/or receive signals on one ormore predefined carrier frequencies. The combination of a sector and aparticular carrier frequency may be referred to herein as a “sectorcarrier.”

A wireless station may have thousands of parameter settings (referred toas “managed objects”). The parameters have to be configured correctly toperform a successful handover. One such parameter includes buildingproper neighbor relations between wireless stations. For example, eachwireless station has particular managed objects, referred to as“frequency objects,” that indicate the neighbor frequencies to bemeasured. Mobile devices then use these configured parameter neighborfrequencies to provide Cell Measurement Reports (CMR) for neighboringcells, which are critical in ensuring mobile devices can be handed offto new wireless stations. As described further below, when a handover isperformed with unfavorable wireless station configurations, the handovermay fail and require a mobile device to re-establish a wirelessconnection with the wireless network. This impacts service quality andmay create unnecessary network congestion.

Configuring frequency objects for wireless stations has typically been amanual and time-consuming process. The manual process is also errorprone. Furthermore, frequency object configurations require updating asnew wireless station sites and carrier frequencies are installed. Thus,there remains a need for a solution to ensure that each wireless stationhas the correct frequency objects configured to allow seamless mobilityactivities to all neighboring cells.

Systems and methods described herein provide automated generation ofneighbor frequency lists for configuration of frequency objects forwireless stations. The systems and methods may predict the neighboringfrequency objects each sector carrier should have configured based onthe transmitting frequencies and configurations of other wirelessstations in a geographic vicinity.

The resulting neighbor frequency list is used to enforce that all of thepredicted frequency objects have been constructed properly across everysector carrier in the network. The neighbor frequency list may beingested as part of a self-organizing network (SON) algorithm forfrequency object enforcement. Using the SON algorithm, configurations ofeach sector carrier are reviewed programmatically, and compared to theresulting neighbor frequency lists generated herein. If the sectorcarriers are misaligned relative to a neighbor frequency, the SON systemmay modify, create, or remove the sector carriers as necessary. Thisallows for handoff to happen between all surrounding frequencies. Oftentimes when carriers are added to one sector, the surrounding sites arenot updated. Without neighbor frequency configurations, the mobiledevices cannot even scan for the adjacent frequencies, which can lead todropped calls if the frequency configurations are missing, or setincorrectly. When a frequency object enforcement process is engaged, thefinal set of changes can be automatically executed with a SON ActivationSession without the need for human involvement. As new sector carriersare added to the network, or spectrum is changed, the neighbor frequencylist will reflect this change and the frequency object enforcement willcontinue to automatically adjust to ensure the network is properlyconfigured for mobility.

According to an implementation, a computing device selects a targetsector carrier of a wireless station in a radio access network andidentifies, based on respective distances from the wireless station,neighboring sector carriers of the target sector carrier. The computingdevice filters the neighboring sector carriers based on an azimuth ofthe target sector carrier to form a filtered set of neighboring sectorcarriers. The computing device calculates a probability of neighboringfrequencies for the target sector carrier based on locations of thefiltered set of neighboring sector carriers and generates, based on thecalculating, a neighbor frequency list for the target sector carrier.The computing device provides the resulting neighbor frequency list to aSON system for use in frequency object configuration for the radioaccess network. The neighbor frequency list is used to configurefrequency objects, for the target sector carrier, that ensure seamlesshandovers within the radio access network

FIG. 1 is a diagram of an exemplary environment 100 in which the systemsand/or methods, described herein, may be implemented. As shown in FIG.1, environment 100 may include user equipment (UE) devices 110-1 to110-X (referred to herein collectively as “UE devices 110” andindividually as “UE device 110”), a radio access network (RAN) 120, acore network 130, a modeling system 140, a data collection system 150,and a SON system 160.

UE device 110 may include any device with long-range (e.g., cellular ormobile wireless network) wireless communication functionality. Forexample, UE device 110 may include a handheld wireless communicationdevice (e.g., a mobile phone, a smart phone, a tablet device, etc.); awearable computer device (e.g., a head-mounted display computer device,a head-mounted camera device, a wristwatch computer device, etc.); alaptop computer, a tablet computer, or another type of portablecomputer; a customer premises equipment (CPE) device, such as a set-topbox or a digital media player, a WiFi access point, a smart television,etc.; a mobile device; a portable gaming system; global positioningsystem (GPS) device; a home appliance device; a home monitoring device;and/or any other type of computer device with wireless communicationcapabilities and a user interface. UE device 110 may includecapabilities for voice communication, mobile broadband services (e.g.,video streaming, real-time gaming, premium Internet access etc.), besteffort data traffic delivery, and/or other types of capabilities. Insome implementations, UE device 110 may communicate usingmachine-to-machine (M2M) communication, such as machine-typecommunication (MTC), and/or another type of M2M communication.

RAN 120 may enable UE devices 110 to connect to core network 130 formobile telephone service, text message services, Internet access, cloudcomputing, and/or other types of data services. RAN 120 may include oneor multiple networks of one or multiple types and technologies. Forexample, RAN 120 may include a Fifth Generation (5G) RAN, a FourthGeneration (4G) RAN, a 4.5G RAN, and/or another type of futuregeneration RAN. By way of further example, RAN 120 may be implemented toinclude a Next Generation (NG) RAN, an Evolved UMTS Terrestrial RadioAccess Network (E-UTRAN) of a Long Term Evolution (LTE) network, anLTE-Advanced (LTE-A) network, and/or an LTE-A Pro network, and/oranother type of RAN (e.g., a legacy RAN).

RAN 120 may include wireless stations 125-1 to 125-N (referred to hereincollectively as “wireless stations 125” and individually as “wirelessstation 125”). Each wireless station 125 may service a set of UE devices110. For example, wireless station 125-1 may service some UE devices 110when the UE devices 110 are located within the geographic area servicedby wireless station 125-1, while other UE devices 110 may be serviced byanother wireless station 125 when the UE devices 110 are located withinthe geographic area serviced by the other wireless station 125.

Depending on the implementation, RAN 120 may include one or multipletypes of wireless stations 125. For example, wireless station 125 mayinclude an evolved Node B (eNB), an evolved Long Term Evolution (eLTE)eNB, a next generation Node B (gNB), a radio network controller (RNC), aremote radio head (RRH), a baseband unit (BBU), a small cell node (e.g.,a picocell device, a femtocell device, a microcell device, a home eNB, arepeater, etc.), or another type of wireless node. Wireless stations 125may connect to core network 130 via backhaul links, such as wired oroptical links. According to various embodiments, RAN 120 may beimplemented according to various wireless technologies (e.g., radioaccess technology (RAT), etc.), wireless standards, wirelessfrequencies/bands, and so forth.

In some embodiments, wireless station 125 may include a one or moreradio frequency (RF) transceivers facing particular directions. Forexample, wireless station 125 may include three RF transceivers and eachRF transceiver may service a 120-degree sector of a 360-degree field ofview. Each RF transceiver may include an antenna array. The antennaarray may include an array of controllable antenna elements configuredto send and receive RF signals via one or more antenna beams. Theantenna elements may be mechanically or digitally controllable to tilt,or adjust the orientation of, an antenna beam in a vertical directionand/or horizontal direction.

Core network 130 may manage communication sessions for UE devices 110.Core network 130 may provide mobility management, session management,authentication, and packet transport, to support UE device 110 andwireless station 125 wireless communications using, for example, a dualconnectivity, multi-RAT configuration. Core network 130 may becompatible with known wireless standards which may include, for example,3GPP 5G, LTE, LTE Advanced, Global System for Mobile Communications(GSM), Universal Mobile Telecommunications System (UMTS), IS-2000, etc.Some or all of core network 130 may be managed by a provider ofcommunication services that also manages RAN 120 and/or UE device 110.Core network 130 may allow the delivery of Internet Protocol (IP)services to UE device 110, and may interface with other externalnetworks. Core network 130 may include one or more server devices and/ornetwork devices, or other types of computation or communication devices(referred to collectively as network devices 135).

Modeling system 140 may include one or more devices, such as computingdevices and/or server devices, which perform modeling of sector carrierneighbor frequencies. For example, modeling system 140 may include oneor more trained machine learning models to convert wireless stationlocation data into spatial coordinates. Modeling system 140 may furtheridentify distances between wireless stations and morphology categoriesfor each sector carrier to determine potential neighbor carrierfrequencies. In some aspects, modeling system 140 may filter the groupof potential neighbor carrier frequencies based on sector carrierdirections. Modeling system 140 may also generate a neighbor frequencylist for each wireless station and sector carrier in RAN 120 ordesignated portions thereof.

Data collection system 150 may collect and store network data for RAN120. For example, data collection system 150 may generate records forwireless stations 125 that include location data, configured sectors,and carrier frequencies. According to implementations described herein,data collection system 150 may provide RAN data to modeling system 140for modeling of sector carrier neighbor frequencies.

SON system 160 may include one or more devices, such as computer devicesand/or server devices, which perform part of self-organization functionsfor access network 120 and/or core network 130. SON system 160 mayobtain information relating to wireless stations 125 and may perform SONactions based on the obtained information. For example, SON system 160may send an instruction to wireless station 125, such as, for example,an instruction to adjust one or more handover parameters, adjust one ormore coverage optimization parameters, adjust one or more powerdistribution parameters, and/or perform another type of adjustment. SONsystem 160 may receive a recommendation (e.g., a neighbor frequency listfor a sector-carrier) from modeling system 140 and may elect to performthe recommended SON adjustment or solicit authorization to perform therecommended SON adjustment. SON system 160 may use neighbor frequencylist for frequency object enforcement. SON system may reviewconfigurations of each sector carrier and compare the configurations tothe neighbor frequency lists. If the sector carriers are misalignedrelative to a neighbor frequency, the SON system may modify, create, orremove sector carriers as necessary.

Although FIG. 1 shows exemplary components of environment 100, in otherimplementations, environment 100 may include fewer components, differentcomponents, differently arranged components, or additional componentsthan depicted in FIG. 1. Additionally or alternatively, one or morecomponents of environment 100 may perform functions described as beingperformed by one or more other components of environment 100.

FIG. 2 is a schematic top view illustrating different sectors andchannels for wireless station 125. As shown in FIG. 2, wireless station125 may include multiple antenna arrays 210-1, 210-2, and 210-3(referred to collectively as “antenna arrays 210” and individually as“antenna array 210”) covering three corresponding sectors 220-1, 220-2,and 220-3 (referred to collectively as “sectors 220” and individually as“sector 220”). Antenna array 210 may include an array of antennaelements configured to send and receive wireless signals. According toan implementation, the antenna elements may be controllable to tilt orsteer an antenna beam in a vertical direction and/or horizontaldirection.

Wireless station 125 may use multiple carrier frequencies, referred toC1, C2, . . . , Cn, within each sector 220. In the example of FIG. 2,wireless station 125 may use antenna arrays 210 to serve three120-degree sectors, where each sector 220 may provide different areas ofcoverage that may overlap. In other implementations, wireless station125 may have more or fewer sectors 220. A particular sector 220 may alsotransmit and/or receive signals on one or more of the predefined carrierfrequencies, C1, C2, . . . , Cn. When associated with a particularsector 220, the carrier frequencies may be generally referred to assector carriers 225.

RF coverage for each sector 220 may be governed by various antenna array210 parameters, including, for example, signal transmit power, awireless station 125/antenna array 210 location, antenna array 210orientation (e.g., antenna heading, elevation, and azimuth), andphysical geography. As shown in FIG. 2, the general direction of eachsector 220 may be defined by an azimuth 230.

FIG. 3A is a diagram illustrating a wireless handover for UE device 110between wireless station 125-1 and wireless station 125-2. While in anactive connection with wireless station 125-1, UE device 110 may provideCell Measurement Reports (CMRs) for neighboring cells to facilitatehandover to a neighboring wireless station 125-2 (and other wirelessstations 125, not shown). As UE device 110 traverses away from wirelessstation 125-1, signal strength 301 from one of antenna arrays 210 (e.g.,210-3 of wireless station 125-1) will decrease and signal strength 302from one of antenna arrays 210 (e.g., 210-1 of wireless station 125-2)will increase. Eventually, wireless station 125-1 and wireless station125-2 agree on a handoff (e.g., when signal strength 302 is establishedas stronger than signal strength 301) for UE device 110. For UE device110 to provide CMRs for relevant cells, wireless stations 125 need to beproperly configured with correct frequency objects (e.g., neighborfrequencies).

FIG. 3B is a diagram illustrating a geographic environment withdifferent morphologies for wireless stations 125. As shown in FIG. 3B, awireless station 125-1 may have multiple neighboring wireless stations125 within an area 310, defined by a radius R, where R may be severalkilometers, for example. Within area 310, population and wirelessstation 125 placement may be reflected in classification of a wirelessstation 125-1 (or a sector carrier 225 of wireless station 125-1) intoone of, for example, three morphologies. A morphology category maygenerally reflect the cell placement density of a surrounding area.According to an implementation, wireless stations 125 may be classifiedas urban (e.g., corresponding to dense wireless station placement, suchas in area 312), suburban (e.g., corresponding to moderately densewireless station placement, such as in area 314), or rural (e.g.,corresponding to sparse wireless station placement, such as in area316). According to implementations described herein, each sector carrier225 of a wireless station 125 may be assigned a morphology category ordesignated as unclassified. The morphology classification may be appliedto give an estimated signal range (e.g., a distance) for each wirelessstation 125/sector carrier 225. In some implementations, differentsector carriers 225 for the same wireless station 125 may have differentmorphology categories.

FIG. 3C is an illustration of a frequency border for two wirelessstations 125-1 and 125-2 according to an implementation. In FIG. 3C,assume wireless station 125-1 has three sectors 220-1, 220-2, and 220-3that use the same two carrier frequencies, C1 and C2. Further assumewireless station 125-2 has three sectors 220-4, 220-5, and 220-6 thatuse two carrier frequencies, C1 and C3. C1, C2, and C3 may correspond,for example, to frequencies within a spectrum assigned to a wirelesscarrier.

In the arrangement of FIG. 3C, sectors 220-1 and 220-2 must considerborder frequency C3 to ensure seamless handovers. Conversely, borderfrequency C3 would not affect sector 220-3. Thus, frequency objects forsectors 220-1 and 220-2 would be configured optimally to solicit CMRsfor border frequency C3, while a frequency object for sectors 220-3could exclude border frequency C3. For wireless station 125-2, sectors220-4 and 220-6 must consider border frequency C2 to ensure seamlesshandovers. While border frequency C2 would not affect sector 220-5.Thus, a frequency object for sectors 220-4 and 220-6 should beconfigured to solicit CMRs for border frequency C2, while a frequencyobject for sectors 220-5 could exclude border frequency C3.

FIG. 4 is a diagram illustrating example components of a device 400according to an implementation described herein. UE device 110, wirelessstation 125, network devices 135, modeling system 140, data collectionsystem 150, SON system 160, and/or other components of networkenvironment 100 may each include one or more devices 400. As shown inFIG. 4, device 400 may include a bus 410, a processor 420, a memory 430,an input device 440, an output device 450, and a communication interface460.

Bus 410 may include a path that permits communication among thecomponents of device 400. Processor 420 may include any type ofsingle-core processor, multi-core processor, microprocessor, latch-basedprocessor, and/or processing logic (or families of processors,microprocessors, and/or processing logics) that interprets and executesinstructions. In other embodiments, processor 420 may include anapplication-specific integrated circuit (ASIC), a field-programmablegate array (FPGA), and/or another type of integrated circuit orprocessing logic.

Memory 430 may include any type of dynamic storage device that may storeinformation and/or instructions, for execution by processor 420, and/orany type of non-volatile storage device that may store information foruse by processor 420. For example, memory 430 may include a randomaccess memory (RAM) or another type of dynamic storage device, aread-only memory (ROM) device or another type of static storage device,a content addressable memory (CAM), a magnetic and/or optical recordingmemory device and its corresponding drive (e.g., a hard disk drive,optical drive, etc.), and/or a removable form of memory, such as a flashmemory.

Input device 440 may allow an operator to input information into device400. Input device 440 may include, for example, a keyboard, a mouse, apen, a microphone, a remote control, an audio capture device, an imageand/or video capture device, a touch-screen display, and/or another typeof input device. In some embodiments, device 400 may be managed remotelyand may not include input device 440.

Output device 450 may output information to an operator of device 400.Output device 450 may include a display, a printer, a speaker, and/oranother type of output device. For example, device 400 may include adisplay, which may include a liquid-crystal display (LCD) for displayingcontent to the customer. In some embodiments, device 400 may be managedremotely and may not include output device 450.

Communication interface 460 may include a transceiver that enablesdevice 400 to communicate with other devices and/or systems via wirelesscommunications (e.g., radio frequency, infrared, and/or visual optics,etc.), wired communications (e.g., conductive wire, twisted pair cable,coaxial cable, transmission line, fiber optic cable, and/or waveguide,etc.), or a combination of wireless and wired communications.Communication interface 460 may include a transmitter that convertsbaseband signals to RF signals and/or a receiver that converts RFsignals to baseband signals. Communication interface 460 may be coupledto one or more antennas/antenna arrays for transmitting and receiving RFsignals.

Communication interface 460 may include a logical component thatincludes input and/or output ports, input and/or output systems, and/orother input and output components that facilitate the transmission ofdata to other devices. For example, communication interface 460 mayinclude a network interface card (e.g., Ethernet card) for wiredcommunications and/or a wireless network interface (e.g., a WiFi) cardfor wireless communications. Communication interface 460 may alsoinclude a universal serial bus (USB) port for communications over acable, a Bluetooth™ wireless interface, a radio-frequency identification(RFID) interface, a near-field communications (NFC) wireless interface,and/or any other type of interface that converts data from one form toanother form.

As will be described in detail below, device 400 may perform certainoperations relating to implementing closed loop analytics feedback for atransport network. Device 400 may perform these operations in responseto processor 420 executing software instructions contained in acomputer-readable medium, such as memory 430. A computer-readable mediummay be defined as a non-transitory memory device. A memory device may beimplemented within a single physical memory device or spread acrossmultiple physical memory devices. The software instructions may be readinto memory 430 from another computer-readable medium or from anotherdevice. The software instructions contained in memory 430 may causeprocessor 420 to perform processes described herein. Alternatively,hardwired circuitry may be used in place of, or in combination with,software instructions to implement processes described herein. Thus,implementations described herein are not limited to any specificcombination of hardware circuitry and software.

Although FIG. 4 shows exemplary components of device 400, in otherimplementations, device 400 may include fewer components, differentcomponents, additional components, or differently arranged componentsthan depicted in FIG. 4. Additionally, or alternatively, one or morecomponents of device 400 may perform one or more tasks described asbeing performed by one or more other components of device 400.

FIG. 5 is a diagram illustrating exemplary logical components ofmodeling system 140. The components of modeling system 140 may beimplemented, for example, via processor 420 executing instructions frommemory 430. Alternatively, some or all of the components of modelingsystem 140 may be implemented via hard-wired circuitry. In someimplementations, one or more components of modeling system 140 mayinclude machine learning models, such as, for example, a K-nearestneighbors classifier, a decision tree classifier, a naïve Bayesclassifier, a support vector machine (SVM) classifier, tree based (e.g.,a random forest) classifier using Euclidian and/or cosine distancemethods, and/or another type of classifier. As shown in FIG. 5, modelingsystem 140 may include a geographical neighbor selector 510, directionalfilter 520, a probability assigner 530, and a sector carrier database(DB) 540.

Geographical neighbor selector 510 may identify a set of wirelessstations 125 that may serve as a potential neighboring wireless stationfor a target wireless station. Geographical neighbor selector 510 mayobtain (e.g., from sector carrier database 540) latitude and longitudecoordinates for wireless stations and convert the latitude and longitudecoordinates to three-dimensional (3D) Cartesian spatial coordinates.Geographical neighbor selector 510 may also obtain (e.g., from sectorcarrier database 540) a morphology category for each wireless station orsector. Based on the morphology category of the target wireless station(e.g., a wireless stations 125 for which neighboring frequencies arebeing determined), geographical neighbor selector 510 may assign arelevant distance to determine a radius for which wireless stations andsector carriers are close enough to the target wireless station to serveas potential neighbors. According to one implementation, geographicalneighbor selector 510 may select up to J nearest neighbors within aradius that is based on the morphology of the target wireless station.Selection of the J closest neighbors may be based on, for example, theEuclidian distance between a target sector carrier and other sectorcarriers. The value of J may be set by a network technician or otherwiseassigned.

FIG. 6A is a sample table indicating different morphology categories anddistance assignments that may be used by geographical neighbor selector510. As shown in FIG. 6A, table 600 includes a morphology field 610, adistance field 620, and a variety of entries 630 for fields 610 and 620.Morphology field 610 may represent a geographical condition associatedwith a wireless station location. Entries 630 for morphology field 610may include urban (e.g., corresponding to dense wireless stationplacement, such as in area 312), suburban (e.g., corresponding tomoderately dense wireless station placement, such as in area 314), rural(e.g., corresponding to sparse wireless station placement, such as inarea 316), and undetermined.

Distance field 620 may provide a corresponding signal range, such as anestimated distance, associated with a morphology type from morphologyfield 610. Entries 630 in distance field 620 may be derived fromheuristics. Each value (e.g., x, y, z) may correspond to a distance(e.g., in kilometers (KM)) of a signal radius for a wireless station.Generally, rural morphology would dictate longer signal ranges thansuburban morphology, and suburban morphology would dictate longer signalranges than urban morphology. Thus, z>y>x in table 600. Wirelessstations with undetermined morphology may be assigned a longest distance(e.g., z). Although table 600 includes three defined morphologycategories, in other implementations, more categories or sub-categoriesmay be used to define signal radii for wireless stations in differentgeographic areas.

Returning to FIG. 5, directional filter 520 may apply directionalcontext to filter candidates identified by geographical neighborselector 510. For example, directional filter 520 may obtain (e.g., fromsector carrier database 540) an azimuth indication for a target sectorcarrier. Using the directional information from the azimuth, directionalfilter 520 may spatially restrict candidates identified by geographicalneighbor selector 510. The spatially restricted area may be equal to orlarger than the angle of the target sector 220. For example, directionalfilter 520 may apply a filter angle over the azimuth that encompassesthe scope of the sector angle. As shown in FIG. 6B, for a given sectorcarrier 225 with an azimuth A and a 120-degree angle, directional filter520 may spatially restrict sector carrier candidates to a 180-degreefilter angle 650 that is bisected by azimuth A. The filtered set ofneighbor frequencies identified by directional filter 520 may be asubset K of the J closest neighbors identified by geographical neighborselector 510.

Probability assigner 530 may assign weights to the filtered group ofneighbor frequencies, K, identified by directional filter 520. For eachtarget sector carrier, probability selector 530 may group the K selectedneighbors (e.g., as provided by directional filter 520) by carrierfrequency. Probability selector 530 may then sort the sector carrierswithin the same carrier frequency group in order of distance (e.g.,distance from the target sector carrier). For each carrier frequencygroup, probability selector 530 may choose the neighboring sectorcarrier that is closest to the target sector carrier. Probabilityselector 530 may then assign a probability for each neighbor carrierfrequency based on physical distances of the chosen sector carriers.

According to one implementation, probability selector 530 may assign aweight for each of the neighboring sector carrier frequencies based onits comparative distance to the target sector carrier. For example, if aneighbor distance is 0, the assigned weight may be 1. Otherwise, theweight may be assigned as a distance ratio. As a simplified exampleillustrated in FIG. 6B, probability assigner 530 may identify theclosest five neighboring sector carriers having different frequencies(N1 through N5) of the spatially restricted sector carrier candidatesfor a target sector carrier 225 with rural morphology (e.g., R=z).Assume each candidate (N) has a distance (d) and frequency (f) asfollows: N1(d1, f1); N2(d2, f2); N3(d2, f3); N4(d3, f4); and N5(d4, f5),where d1>d2>d3>d4. In this example, the weight assigned to the carrierfrequency of N5 may be greater than the weight assigned to the carrierfrequency of N4, which may greater than the weight assigned to N3. Theweight assigned to N3 may be equal to N2, which may be greater than theweight assigned to N1.

Probability assigner 530 may generate, based on the weighted neighborfrequencies, a neighbor frequency list for the target carrier sector. Asdescribed above, the neighbor frequency list may be provided, forexample, to SON system 160 for configuration of frequency objects foreach sector carrier 225 of wireless stations 125.

Sector carrier database 540 may store information relating to sectorcarriers associated with wireless stations 125. According to animplementation, data for sector carrier database 540 may be populated bydata collection system 150. Sector carrier database 540 may be accessedby geographical neighbor selector 510, directional filter 520, andprobability assigner 530 to generate information for frequency objects.Exemplary information that may be stored in sector carrier database 540is described below with reference to FIG. 7.

FIG. 7 is a diagram illustrating an exemplary data structure of sectorcarrier database 540, according to an implementation described herein.As shown in FIG. 7, sector carrier database 540 may include one or morewireless station sector records 700. Each wireless station sector record700 may store information relating to a particular wireless stationsector carrier In the example of FIG. 7, each wireless station sectorrecord 700 may include a market ID field 705, a wireless station (WS) IDfield 710, a sector number field 715, a carrier number field 720, anazimuth degree field 725, a morphology field 730, a latitude field 735,a longitude field 740, and a downlink (DL) frequency field 745.

Market ID field 705 may indicate a geographic area or region asdesignated by a mobile carrier or agency regulation. A market ID may,for example, be allocated for certain wireless spectrum. Wirelessstation ID field 710 may include a unique identifier for a wirelessstation 125 in record 700. Sector number field 715 may include a sectordesignation for a sector 220 of the wireless station 125. Carrier numberfield 720 may include a carrier designation for a carrier used in thecorresponding sector 220 of the wireless station 125 in record 700.

Azimuth degree field 725 may include an azimuth direction for thecorresponding sector 220 of wireless station 125 in record 700.Morphology field 730 may include a geographic type designation (e.g.,Urban, Suburban, Rural, etc.) corresponding to the wireless station 125in record 700. Latitude field 735 and a longitude field 740 may includelatitude and longitude coordinates, respectively, for the correspondingwireless station 125 of record 700. The latitude and longitudecoordinates may be converted (e.g., by geographical neighbor selector510) into spatial distance coordinates. DL frequency field 745 mayinclude a downlink frequency used by the corresponding wireless station125 of record 700.

Although FIG. 7 shows exemplary fields of sector carrier database 540,in other implementations, sector carrier database 540 may include fewerfields, different fields, or additional fields than depicted in FIG. 7.For example, some fields shown in sector carrier database 540 may becombined or similar information may be represented with differentindicators.

FIG. 8 is a flow diagram illustrating an exemplary process 800 forgenerating a neighboring frequency list for a target carrier sector,according to an implementation described herein. In one implementation,process 800 may be implemented by modeling system 140. In anotherimplementation, process 800 may be implemented by modeling system 140 inconjunction with one or more other network devices in networkenvironment 100.

Process 800 may include collecting or retrieving sector carrier data fora RAN segment (block 810), and converting wireless station location datato spatial coordinates (block 820). For example, modeling system 140 maycollect or retrieve wireless station data for all or a portion ofwireless stations 125 in RAN 120. Modeling system 140 may store the datain sector carrier database 540, including, for example, wireless stationsector records 700. According to an implementation, records 700 may besegmented by market identifiers (e.g., market ID field 705) Geographicalneighbor selector 510 may convert some or all of the latitude andlongitude coordinates in wireless station sector records 700 tothree-dimensional Cartesian spatial coordinates. For example,geographical neighbor selector 510 may convert coordinates for records700 that have the same market identifier in market ID field 705.

Process 800 may further include selecting a target sector carrier (block830), and identifying neighboring sector carriers based on morphology(block 840). For example, using a random or ordered sequence,geographical neighbor selector 510 may select one of records 700 as atarget sector carrier. Alternatively, a network technician may identifya wireless station or sector carrier, and geographical neighbor selector510 may select the corresponding record 700. Geographical neighborselector 510 may identify from sector carrier records 700 a morphologycategory (e.g., from morphology field 730) for each wireless station orsector in the RAN segment. Based on the morphology category,geographical neighbor selector 510 may assign a relevant signal range(or distance) to each sector carrier to determine which wirelessstations are close enough to potentially serve as neighboring wirelessstations to the target sector carrier.

Process 800 may also include filtering neighboring sector carriers basedon an azimuth of the target sector carrier (block 850). For example,directional filter 520 may obtain (e.g., from sector carrier database540) an azimuth indication for the target sector carrier. Using theazimuth, directional filter 520 may spatially restrict the neighborsector carrier candidates identified by geographical neighbor selector510. For example, for a 120-degree sector 220, directional filter 520may filter out neighbor sector carriers outside a 180-degree area thatis orthogonal to the azimuth.

Process 800 may additionally include calculating probabilities of thetarget sector carrier's neighboring frequencies based on distance (block860), and generating a neighbor frequency list for the target sectorcarrier (block 870). For example, probability selector 530 may choosethe nearest sector carriers from each group of sector carriers havingthe same frequency. Probability selector 530 may then assign a weightfor each neighbor carrier frequency based on physical distances of thechosen sector carriers. Probability assigner 530 may generate, based onthe weighted neighbor frequencies, a neighbor frequency list for thetarget carrier sector. The neighbor frequency list may be provided, forexample, to SON system 160.

Process 800 may determine if there are more target sector carriers inthe RAN segment (block 880). If so (block 880—Yes), process 800 mayreturn to block 830 to select a next target sector carrier. If there areno more target sector carriers in the RAN segment (block 880—No),process 800 may include applying the neighbor frequency lists in a SONsystem for the RAN segment (block 890). For example, modeling system 140(e.g., probability assigner 530) may provide to SON system 160 neighborfrequency lists for each sector carrier 225 in all or a portion ofwireless stations 125 in RAN 120. SON system 160 may compare theneighbor frequency lists against each sector carrier to configure thefrequency objects that indicate neighbor frequencies to be measured foreach sector carrier 225. SON system 160 may adjust sector carrierconfigurations to ensure all neighbor frequencies are accounted for ateach sector carrier 225. SON system 160 may use a frequency objectenforcement procedure to execute a final set of changes in RAN 120

As set forth in this description and illustrated by the drawings,reference is made to “an exemplary embodiment,” “an embodiment,”“embodiments,” etc., which may include a particular feature, structureor characteristic in connection with an embodiment(s). However, the useof the phrase or term “an embodiment,” “embodiments,” etc., in variousplaces in the specification does not necessarily refer to allembodiments described, nor does it necessarily refer to the sameembodiment, nor are separate or alternative embodiments necessarilymutually exclusive of other embodiment(s). The same applies to the term“implementation,” “implementations,” etc.

The foregoing description of embodiments provides illustration, but isnot intended to be exhaustive or to limit the embodiments to the preciseform disclosed. Accordingly, modifications to the embodiments describedherein may be possible. The description and drawings are accordingly tobe regarded as illustrative rather than restrictive.

The terms “a,” “an,” and “the” are intended to be interpreted to includeone or more items. Further, the phrase “based on” is intended to beinterpreted as “based, at least in part, on,” unless explicitly statedotherwise. The term “and/or” is intended to be interpreted to includeany and all combinations of one or more of the associated items. Theword “exemplary” is used herein to mean “serving as an example.” Anyembodiment or implementation described as “exemplary” is not necessarilyto be construed as preferred or advantageous over other embodiments orimplementations.

In addition, while series of blocks have been described with regard tothe processes illustrated in FIG. 8, the order of the blocks may bemodified according to other embodiments. Further, non-dependent blocksmay be performed in parallel. Additionally, other processes described inthis description may be modified and/or non-dependent operations may beperformed in parallel.

Embodiments described herein may be implemented in many different formsof software executed by hardware. For example, a process or a functionmay be implemented as “logic,” a “component,” or an “element.” Thelogic, the component, or the element, may include, for example, hardware(e.g., processor 320, etc.), or a combination of hardware and software.

Embodiments have been described without reference to the specificsoftware code because the software code can be designed to implement theembodiments based on the description herein and commercially availablesoftware design environments and/or languages. For example, varioustypes of programming languages including, for example, a compiledlanguage, an interpreted language, a declarative language, or aprocedural language may be implemented.

Use of ordinal terms such as “first,” “second,” “third,” etc., in theclaims to modify a claim element does not by itself connote anypriority, precedence, or order of one claim element over another, thetemporal order in which acts of a method are performed, the temporalorder in which instructions executed by a device are performed, etc.,but are used merely as labels to distinguish one claim element having acertain name from another element having a same name (but for use of theordinal term) to distinguish the claim elements.

Additionally, embodiments described herein may be implemented as anon-transitory computer-readable storage medium that stores data and/orinformation, such as instructions, program code, a data structure, aprogram module, an application, a script, or other known or conventionalform suitable for use in a computing environment. The program code,instructions, application, etc., is readable and executable by aprocessor (e.g., processor 420) of a device. A non-transitory storagemedium includes one or more of the storage mediums described in relationto memory 430.

To the extent the aforementioned embodiments collect, store or employpersonal information of individuals, it should be understood that suchinformation shall be collected, stored and used in accordance with allapplicable laws concerning protection of personal information.Additionally, the collection, storage and use of such information may besubject to consent of the individual to such activity, for example,through well known “opt-in” or “opt-out” processes as may be appropriatefor the situation and type of information. Storage and use of personalinformation may be in an appropriately secure manner reflective of thetype of information, for example, through various encryption andanonymization techniques for particularly sensitive information.

No element, act, or instruction set forth in this description should beconstrued as critical or essential to the embodiments described hereinunless explicitly indicated as such. All structural and functionalequivalents to the elements of the various aspects set forth in thisdisclosure that are known or later come to be known are expresslyincorporated herein by reference and are intended to be encompassed bythe claims.

What is claimed is:
 1. A method comprising: selecting, by a computingdevice, a target sector carrier of a wireless station of multiplewireless stations in a radio access network; identifying, by thecomputing device and based on distances from the wireless station,neighboring sector carriers of the target sector carrier; filtering, bythe computing device, the neighboring sector carriers based on anazimuth of the target sector carrier to form a filtered set ofneighboring sector carriers; calculating, by the computing device, aprobability of neighboring frequencies for the target sector carrierbased on locations of the filtered set of neighboring sector carriers;generating, by the computing device and based on the calculating, aneighbor frequency list for the target sector carrier; and configuring,based on the neighbor frequency lists, frequency objects for the targetsector carrier.
 2. The method of claim 1, further comprising:retrieving, by a computing device, device data for the multiple wirelessstations, wherein the device data includes location data, a sectorazimuth, and a carrier frequency for each sector carrier of eachwireless station of the multiple wireless stations.
 3. The method ofclaim 2, wherein the location data comprises latitude and longitudecoordinates, and wherein the method further comprises: converting, bythe computing device, the location data for each of the wirelessstations into spatial coordinates.
 4. The method of claim 1, wherein theneighboring sector carriers are identified based on a distance betweenwireless stations and a morphology category of the target sectorcarrier.
 5. The method of claim 4, wherein the morphology classificationcorresponds to a signal range.
 6. The method of claim 5, wherein thesignal range is heuristically determined.
 7. The method of claim 1,wherein filtering the neighboring sector carriers comprises: identifyingthe azimuth and a sector angle of the target sector carrier; applying afilter angle that encompasses the scope of the sector angle; selectingwireless stations, for the neighboring sector carriers, that are withinthe filter angle; and identifying, from the selected wireless stations,the set of neighboring sector carriers.
 8. The method of claim 1,wherein calculating the probability of neighboring frequencies for thetarget sector carrier comprises: grouping the set of neighboring sectorcarriers into common frequency groups, choosing, from each of the commonfrequency groups, the nearest neighboring sector carrier, and assigninga weight to each different neighboring frequency based on relativedistance of the nearest neighboring sector carriers to the target sectorcarrier.
 9. The method of claim 1, wherein the multiple wirelessstations include one or more of an evolved Node B (eNB) and a nextgeneration Node B (gNB).
 10. The method of claim 1, wherein theneighboring sector carriers are identified based on an estimated signalrange of each sector carrier of the multiple wireless stations.
 11. Acomputing device, comprising: a memory storing instructions; and aprocessor, coupled to the memory, configured to execute the instructionsto: select a target sector carrier of a wireless station of multiplewireless stations in a radio access network; identify, based ondistances from the wireless station, neighboring sector carriers of thetarget sector carrier; filter the neighboring sector carriers based onan azimuth of the target sector carrier to form a filtered set ofneighboring sector carriers; calculate a probability of neighboringfrequencies for the target sector carrier based on locations of thefiltered set of neighboring sector carriers; generate, based on thecalculating, a neighbor frequency list for the target sector carrier;and configure, based on the neighbor frequency lists, frequency objectsfor the target sector carrier.
 12. The computing device of claim 11,wherein the processor is further configured to execute the instructionsto: retrieve device data for the multiple wireless stations, wherein thedevice data includes location data, a sector azimuth, and a carrierfrequency for each sector carrier of each wireless station of themultiple wireless stations.
 13. The computing device of claim 12,wherein the location data comprises latitude and longitude coordinates,and wherein the processor is further configured to execute theinstructions to: convert the location data for each of the wirelessstations into spatial coordinates.
 14. The computing device of claim 11,wherein the neighboring sector carriers are identified based on adistance between wireless stations and a morphology category of eachsector carrier of the multiple wireless stations.
 15. The computingdevice of claim 11, wherein, when filtering the neighboring sectorcarriers, the processors is further configured to execute theinstructions to: identify the azimuth of the target sector carrier;select wireless stations, for the neighboring sector carriers, that arewithin a filter angle that is at least equal to a sector angle of thetarget sector carrier; and identify, from the selected wirelessstations, the set of neighboring sector carriers.
 16. The computingdevice of claim 11, wherein, when calculating the probability ofneighboring frequencies, the processor is further configured to executethe instructions to: determine a distance between wireless stations forthe target sector carrier and each one of the neighboring sectorcarriers, choose a nearest neighboring sector carriers for eachdifferent neighboring frequency; and assign a weight to each differentneighboring frequency based on relative distance of the chosenneighboring sector carriers to the target sector carrier.
 17. Thecomputing device of claim 16, wherein the multiple wireless stationsinclude an evolved Node B (eNB).
 18. A non-transitory computer-readablemedium storing instructions executable by one or more processors, theinstructions comprising: selecting a target sector carrier of a wirelessstation of multiple wireless stations in a radio access network;identifying, based on distances from the wireless station, neighboringsector carriers of the target sector carrier; filtering the neighboringsector carriers based on an azimuth of the target sector carrier to forma filtered set of neighboring sector carriers; calculating a probabilityof neighboring frequencies for the target sector carrier based onlocations of the filtered set of neighboring sector carriers; generatingbased on the calculating, a neighbor frequency list for the targetsector carrier; and configure, based on the neighbor frequency lists,frequency objects for the target sector carrier.
 19. The non-transitorycomputer-readable medium of claim 18, the instructions thithercomprising: storing device data for the multiple wireless stations,wherein the device data includes location data, a morphology category, asector azimuth, and a carrier frequency for each sector carrier of eachwireless station of the multiple wireless stations.
 20. Thenon-transitory computer-readable medium of claim 18, the instructionsfurther comprising: converting the location data for each of thewireless stations into spatial coordinates.